Co-channel interference (CCI) and intersymbol interference (ISI) are two major impediments limiting the capacity of cellular networks. Co-channel interference results when two or more simultaneous transmissions occur on the same frequency. In the case of TDMA systems, such as GSM/EDGE systems, co-channel interference is primarily due to frequency reuse. In order to increase system capacity, a radio frequency carrier is reused in multiple cells. A signal received by a receiver will contain not only the desired signal, but will also contain unwanted signals from other co-channel cells. The minimum distance between co-channel cells is dependent on the maximum tolerable co-channel interference at the receiver. Receivers resistant to co-channel interference allow greater frequency reuse and hence greater system capacity.
Diversity techniques have been used to mitigate co-channel interference. One form a diversity is known as receiver diversity. A receiver with two or more antennas receives the desired signal over independently fading channels. Since the same interfering signals are present in the signal received over each diversity path, the received signals can be combined to suppress CCI and improve the signal to noise ratio (SNR).
ISI is caused by multipath fading in bandlimited, time-dispersive channels. ISI distorts the transmitted signal so that adjacent pulses overlap one another. ISI has been recognized as a major obstacle to high speed data transmission over mobile radio channels. ISI is mitigated by equalizers at the receiver. The optimal receiver for detection of a received signal in the presence of ISI is the maximum likelihood sequence estimation (MLSE) receiver. Instead of estimating each symbol individually, a MLSE receiver attempts to find the sequence with the greatest probability of being correct. Using an estimate of the channel, the MLSE receiver computes a likelihood metric for hypothesized sequences and chooses the sequence that produces the maximum likelihood metric. Using the MLSE for equalization was first proposed by Forney in Maximum-Likelihood Sequence Estimation of Digital Sequences in the Presence of Intersymbol Interference, IEEE Trans. Info. Theory, vol. IT-18, pp. 363-378, May 1972, and is further explored by Ungerboeck in Adaptive Maximum-Likelihood Reciever for Carrier Modulated Data-Transmission Systems, IEEE Transactions On Communications, vol COM-22, pp. 624-636, May 1974.
A MLSE receiver may be implemented using the Viterbi algorithm. The Viterbi algorithm is a recursive technique that simplifies the problem of finding the shortest path through a trellis. Each path through the trellis corresponds to one possible transmitted sequence and each branch corresponds to a possible transmitted symbol. Each branch of the trellis is assigned a branch metric that represents the likelihood that the corresponding symbol is part of the transmitted sequence. A path metric is then computed for a path by summing the branch metrics comprising the path. The path that most closely matches the received symbol sequence is the one with the lowest path metric.
The way that the equalizer is implemented has a significant impact on the cost of the wireless receiver, since the equalizer complexity typically comprises a substantial portion of the overall receiver complexity. For equalizers implemented using the Viterbi algorithm, their complexity depends on the complexity of the metric used to evaluate the likelihoods of different hypothesized sequences.
For single-antenna receivers, two well-known equalizer metrics are the Forney metric and the Ungerboeck metric. Though the two metrics are equivalent, the Forney metric has the advantage of being geometrically intuitive and easy to compute, since it is based on the Euclidean distance. In addition, most of the practical reduced-complexity equalization techniques found in the literature, such as the decision feedback sequence estimation (DFSE) equalizers and reduced-state sequence estimation (RSSE) equalizers, are based on the Forney metric.
For multiple-antenna receivers, the Forney equalizer metric is typically implemented as the sum of squared Euclidean distances over the received signals from all antennas. This implementation is known as metric combining. As a result, the equalizer complexity increases directly with the number of antennas when the Forney metric is used. When the Ungerboeck metric is used in a multiple antenna receiver, the received signals from each antenna are matched filtered and combined before the branch metric is computed. Thus, the complexity of the equalizer is independent of the number of antennas when the Ungerboeck metric is used. However, the Ungerboeck metric is more computationally complex than the Forney metric, and there are fewer reduced complexity techniques based on the Ungerboeck metric.